In many computer graphics, computer animation, computer vision, robotics, virtual reality, and visual effects applications, it is frequently necessary to relight images of objects from a known object class. The images can be acquired by a real camera, or the images can be synthetically generated by a virtual camera, e.g., in computer graphics and animation. An object class refers to a set of objects that are similar in shape and appearance such as human faces, bodies, cars, metal screws, etc.
Methods are known for recognizing objects within an object class, such as identifying particular faces or people in images. The methods typically compare an image of an unknown object from the class to images of known objects from the class in a database. As defined herein, a gallery of images refers to the set of images of the known objects from the class that is stored in the database, and a probe image refers to the image of the unknown object from the class. Determining whether two images are of the same object is an especially difficult problem when the images are taken under very different illuminations. This is a particularly difficult problem for face recognition systems.
There are three main approaches to deal with this problem of varying illumination. The first approach is based on constructing a classifier that uses illumination-invariant image features. The second approach normalizes the images in an attempt to explicitly reduce or remove the effects of varying illumination. Techniques from the second approach either assume a reflectance model and attempt to remove the effects of light, or adopt an image processing approach in which various steps are empirically chosen to provide desired output. The third approach attempts to generate synthetic relit images that generalize from a given gallery of images to match a wide range of possible illumination variations in probe images. The synthetic images are added to the gallery to produce an augmented database. It is expected that each probe image will find a close match to at least one of the images in the augmented gallery. Although this third approach can be effective at generating good quality relit images, the prior art relighting methods suffer from the requirements of manual initialization and cumbersome optimization, which reduce their attractiveness for face recognition and other applications that require fast performance or have a large gallery.